In this contributed article, Kieron-Sambrook Smith, Chief Commercial Officer for Platform.sh, discusses why enterprises tend to lose the forest (business goals) for the trees (container management), and the strategies they can implement to stop distracting their developers from doing what they do best: writing killer code.

This insideBIGDATA technology guide explores how current implementations for AI and DL applications can be deployed using new storage architectures and protocols specifically designed to deliver data with high-throughput, low-latency and maximum concurrency.

With the current maturation of Artificial Intelligence applications and Deep Learning algorithms, many organizations are spinning up initiatives to figure out how they will extract competitive differentiation from their data. This guest article comes from DDN Storage, a provider of high performance, high capacity big data storage systems, processing solutions and services to data-intensive, global organizations.

Cazena unveiled its Data Science Sandbox as a Service. The service is designed to deliver significantly faster outcomes from data science and analytics programs. Now, data scientists can run a wide range of analytics in a flexible cloud environment without having to build, manage or maintain the underlying technology.

At the heart of a big data environment is a big data platform, which does all the heavy lifting recquired to capture, store, transform, and govern large volumes of multi-structured data at high speeds. Big Data platforms process data in batch or real time using both relational and non-relational database engines, languages, components, and techniques.

Industry Perspectives

In this special guest feature, Sean McDermott, CEO and founder of Windward Consulting Group and RedMonocle, offers what enterprises need to know about the five levels of AIOps maturity. When maneuvering through each level, keep the long-term AIOps strategy and goals at the center to achieve the true potential of AIOps.

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White Papers

In this short eBook, you’ll discover automated machine learning using H2O.ai. H2O.ai has dedicated itself to democratizing all aspects of AI, including machine
learning. H2O Driverless AI is a machine learning solution that automates AI for nontechnical
users. So-called “AutoML” solutions like H2O Driverless AI are rising in popularity for enterprises across a wide range of industries. With it, users can build robust, fast, and accurate machine learning solutions. It also includes visualization and interpretability features that explain the data modeling results in plain English, fostering further adoption and trust in AI.